Holm Ivar, Fridolfsson Jonatan, Börjesson Mats, Arvidsson Daniel
Center for Health and Performance, Department of Food and Nutrition, and Sport Science, Faculty of Education, University of Gothenburg, Gothenburg, Sweden.
Center for Health and Performance, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
BMC Biomed Eng. 2023 Apr 14;5(1):3. doi: 10.1186/s42490-023-00071-9.
The number of steps by an individual, has traditionally been assessed with a pedometer, but increasingly with an accelerometer. The ActiLife software (AL) is the most common way to process accelerometer data to steps, but it is not open source which could aid understanding of measurement errors. The aim of this study was to compare assessment of steps from the open-source algorithm part of the GGIR package and two closed algorithms, AL normal (n) and low frequency extension (lfe) algorithms to Yamax pedometer, as reference. Free-living in healthy adults with a wide range of activity level was studied.
A total 46 participants divided by activity level into a low-medium active group and a high active group, wore both an accelerometer and a pedometer for 14 days. In total 614 complete days were analyzed. A significant correlation between Yamax and all three algorithms was shown but all comparisons were significantly different with paired t-tests except for ALn vs Yamax. The mean bias shows that ALn slightly overestimated steps in the low-medium active group and slightly underestimated steps in high active group. The mean percentage error (MAPE) was 17% and 9% respectively. The ALlfe overestimated steps by approximately 6700/day in both groups and the MAPE was 88% in the low-medium active group and 43% in the high active group. The open-source algorithm underestimated steps with a systematic error related to activity level. The MAPE was 28% in the low-medium active group and 48% in the high active group.
The open-source algorithm captures steps fairly well in low-medium active individuals when comparing with Yamax pedometer, but did not show satisfactory results in more active individuals, indicating that it must be modified before implemented in population research. The AL algorithm without the low frequency extension measures similar number of steps as Yamax in free-living and is a useful alternative before a valid open-source algorithm is available.
个人的步数传统上是用计步器来评估的，但现在越来越多地使用加速度计。ActiLife软件（AL）是将加速度计数据处理为步数最常用的方法，但它不是开源的，而开源软件有助于理解测量误差。本研究的目的是将GGIR软件包中的开源算法部分以及两种封闭算法（AL正常（n）算法和低频扩展（lfe）算法）得出的步数评估结果与作为参考的Yamax计步器进行比较。对活动水平范围广泛的健康成年人的日常活动情况进行了研究。
总共46名参与者按活动水平分为低-中度活动组和高活动组，他们佩戴加速度计和计步器14天。总共分析了614个完整日的数据。结果显示Yamax与所有三种算法之间均存在显著相关性，但除了ALn与Yamax之间的比较外，所有配对t检验的比较结果均存在显著差异。平均偏差表明，ALn在低-中度活动组中略微高估了步数，而在高活动组中略微低估了步数。平均百分比误差（MAPE）分别为17%和9%。ALlfe在两组中均高估了约6700步/天，低-中度活动组的MAPE为88%，高活动组为43%。开源算法低估了步数，且存在与活动水平相关的系统误差。低-中度活动组的MAPE为28%，高活动组为48%。
与Yamax计步器相比，开源算法在低-中度活动个体中能较好地记录步数，但在活动量较大的个体中未显示出令人满意的结果，这表明在人群研究中应用之前必须对其进行修改。没有低频扩展的AL算法在日常活动中记录的步数与Yamax相似，在有效的开源算法出现之前，它是一个有用的替代方法。
